KU Leuven Research Data Repository
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Replication Data for: Inflammatory profile during normothermic kidney perfusion with whole blood versus red blood cell perfusion fluid
No full textThis dataset contains replication data (this is processed data) collected during normothermic perfusion of pig kidneys
Replication Data for: Investigating methods to improve photovoltaic thermal models at second-to-minute timescales
No full textKU Leuven Technology Campus Ghent rooftop PV array weather data, used for the paper "Investigating methods to improve photovoltaic thermal models at second-to-minute timescales", doi: https://doi.org/10.1016/j.solener.2023.111889.
This file contains raw data measured and stored at 1 s time resolution.
Occasional data loss may be present; night-time data loss of ~10-20 s is due to the daily saving of the data.
Original files were TDMS.
Daily TDMS files converted in python and pandas to pandas dataframes (including time format conversion from Labview TDMS to python datetime), and concatenated to a continuous file.
More information on the measurement set-up and data can be found in "Outdoor thermal and electrical characterisation of photovoltaic modules and systems", Bert Herteleer, PhD thesis 2016, KU Leuven.
Data: from 1 May 2015 to 23 March 2016. On 24 March the cup anemometer was permanently damaged, and eventually replaced.
Columns: datetime, Tamb, PV052_5x4C, PV052_5x4BS, G_Pyr_18_S, WSdir, WS
datetime: pandas generated datetime, yyyy-mm-dd HH:MM:SS (original was Labview-generated timestamp).
Tamb: ambient temperature, calibrated Thies Pt100 sensor 2.1280.00.000 in Weather and Thermal Radiation Shield, compact 1.1025.55.000, total combined uncertainty (k=2) = 0.4 K
PV052_5x4C: PV module PV052, centre-of-module Pt100 sensor laminated against the cell, total combined uncertainty (k=2) = 0.4 K
PV052_5x4BS: PV module PV052, centre-of-module Pt100 sensor on the backsheet next to the cell sensor, total combined uncertainty (k=2) = 0.4 K
G_Pyr_18_S: calibrated pyranometer Kipp & Zonen CMP11, in the plane of the array (18 degree tilt, due South); calibration uncertainty 1.4%
WSdir: wind direction, measured using weather vane: uncertainty 5 degrees
WS: wind speed, measured using calibrated : Thies First Class Advanced cup anemometer: uncertainty 0.2 m/s between 0.3 m/s and 50 m/s
Replication Data for: Drinking Gesture Detection Using Wrist-Worn IMU Sensors with Multi-Stage Temporal Convolutional Network in Free-Living Environments
No full textThe dataset that contains IMU signal and the related annotation for drinking activity in free-living environment. It contains raw data (.csv) for DX-I (semi-controlled) and DX-II (free-living), and also the ready-to-use data (.pkl).
The DX-I dataset contains 8.9h IMU data from 13 participants collected in semi-controlled environment. The DX-II dataset contains 45.2h IMU data from 7 participants collected in free-living environment
Replication Data for: Accuracy-speed-stability trade-offs in a targeted stepping task are similar in young and older adults
No full textData set of 25 young and 25 older adults performed a target stepping task. In this study we aimed to test whether accuracy-speed-stability trade-offs are larger in older compared to young individuals. Since sensorimotor function degrades with age, we hypothesized that poor sensorimotor function would be associated with larger accuracy-speed-stability trade-offs. The dataset contains marker and force plate data from subjects performing a stepping task in which they stepped into projected targets starting from quiet standing. Additionally, we quantified sensorimotor function by measuring motor variability using a torque matching task on the Biodex, proprioception using a joint position matching task, and reliance on sensory information during standing using the Sensory Organization Test on the NeuroCom balance master
Replication data for Eeklo Footbridge: Benchmark Dataset on Pedestrian-Induced Vibrations
No full textThe Eeklo footbridge benchmark dataset was collected on October 26th 2017 on the Eeklo footbridge (Belgium) and involves unrestricted pedestrian traffic. Two pedestrian densities were considered: 0.25 persons/m2 and 0.50 persons/m2, corresponding to approximately 75 and 150 pedestrians. All procedures were approved by the social and societal ethics committee of KU Leuven and each participant gave written informed consent prior to participation. The following data
is available: (1) Structural accelerations at 10 locations along the bridge deck, (2) Accelerations levels at the lower back of each participant, (3) Pedestrian trajectories along the bridge deck and (4) a digital twin representing the dynamic behavior of the footbridge
MEP data PhD project: interpersonal motor resonance during social gaze conditions
No full textThis dataset contains corticospinal excitability measures (MEPs, rMT, background EMG) investigated via single-pulse transcranial magnetic stimulation (TMS) of 166 anonymized adult participants (141 neurotypical, 25 with a clinical autism spectrum diagnosis) included in the PhD project of the dataset author. TMS-induced MEPs were collected during rest and during different action observation settings (see ReadMe files per cohort for experimental details and stimuli descriptions). The dataset is structured per experimental cohort (1 to 6) and composed of 1 CSV file per participant, reporting raw MEP waveform characteristics and background EMG-related parameters elicited under different experimental conditions.Participants' demographic characteristics, individual TMS-related parameters (rMT, hotspot coordinates) as well as self-reported raw questionnaire scores are reported in the tabular data overview. Questionnaires include the Social Responsiveness Scale (SRS), Social Phobia Inventory (SPIN) and State Adult Attachment Scale (SAAM)
Replication Data for: The magmatic-hydrothermal transition in pegmatites and its effects on rare-metal mineralization: Insights from melt-crystal-fluid interactions in phosphate minerals from the Buranga dike (Rwanda) - PhD Thesis
No full textThe enclosed datasets were acquired as part of the research of Fernando Prado Araujo towards obtaining the degree of Doctor of Science (PhD): Geology at the KU Leuven, Belgium.
The purpose of the analyses is to understand the processes occurring during the transition from a melt- to a fluid-dominated environment in phosphorus-rich rare-element pegmatites.
All measurements were performed in natural rock and mineral samples from the Buranga pegmatite, western Rwanda.
Specifications of each dataset and measurement method are provided in the following sections
Replication Data for ARA-PEPs: a repository of putative sORF-encoded peptides in Arabidopsis thaliana
No full textMany eukaryotic RNAs have been considered non-coding as they only contain short open reading frames (sORFs). However, there is increasing evidence for the translation of these sORFs into bioactive peptides. To aid the functional annotation of these peptides, we have developed a repository of putative peptides encoded by sORFs in the A. thaliana genome starting from in-house Tiling arrays, RNA-seq data and other publicly available datasets. In addition, we have carried out large-scale docking experiments to provide clues to the molecular function of these peptides
Interview Transcripts of Students' and Professionals' Perception of Creativity
No full textDataset containing fully anonymized transcripts of interviews of students and professionals concerning their perception of creativity in software engineering. Includes scripts used to generate key code trees and saturation graphs
A Survey of Methods and Input Data Types for House Price Prediction: Literature list
No full textGeneral file description
This xlsx document contains the literature list that forms the basis of the paper 'A Survey of Methods and Input Data Types for House Price Prediction' by Geerts, M., vanden Broucke, S. and De Weerdt, J. The Excel document contains seven sheets, relating to the phases described in the survey.
Phase3
This sheet contains the literature list for the end of Phase 2 and the start of Phase 3. It has 590 rows and 19 columns. Each row contains the citation information of one article. The columns describe the ID, Authors, Title, Year, Source title, Volume, Issue, DOI, ISSN, ISBN, PubMed, Publisher, Document Type, Language, Keywords, Link, Book DOI, Algorithmic (Title) and Algorithmic (Abstract). The latter two columns are used to indicate whether the articles describe an algorithmic approach to predict house prices based on the title and the abstract respectively. These two columns take the values 'Yes', 'No', and 'Maybe', and were completed during Phase 3.
Phase4
This sheet contains the literature list for the end of Phase 3 and the start of Phase 4. It has 116 rows and 20 columns. Each row contains the citation information of one article. The columns describe the ID, Authors, Title, Year, Source title, Volume, Issue, DOI, ISSN, ISBN, PubMed, Publisher, Document Type, Language, Keywords, Link, Book DOI, Algorithmic (Title), Algorithmic (Abstract) and Reading. All columns are the same as in the first sheet, except for the three last columns. The columns Algorithmic (Title) and Algorithmic (Abstract) now only contain the value 'Yes' as only the articles that describe an algorithm are retained in Phase 3. The column Reading describes the outcome of Phase 4. This columns is empty if the article is retained in this phase and describes the reason if it is not retained.
Phase4(end)
This sheet contains the literature list for the end of Phase 4. It has 94 rows and 20 columns. Each row contains the citation information of one article. The columns describe the ID, Authors, Title, Year, Source title, Volume, Issue, DOI, ISSN, ISBN, PubMed, Publisher, Document Type, Language, Keywords, Link, Book DOI, Algorithmic (Title), Algorithmic (Abstract) and Reading. All columns are the same as in the second sheet. The column Reading is now empty because the articles that were not retained in Phase 4 are removed from the list.
Data table
This sheet contains a table of the literature at the end of Phase 4 with indications of input data types used in the articles, the data novelty score and the cluster that the articles belong to. It has 95 rows, where each row contains the information of one article, except the last 'Total' row. It contains 21 columns :
ID: This is the same identifier as in the previous sheets.
Column1: This is a new identifier, based on an ordering on year and author.
Authors: Same as before.
Title: Same as before.
Year: Same as before.
Structural, Temporal data, Socioeconomic, Environmental, POI, Basic spatial, Location, Eucl Distances, Adv Spatial, Network Distance, Topographical data, Graphs, Images, Text: These are the different input data types. The cell is filled with 'X' if the corresponding article is using the input data type described in the column name.
Score: This column indicates the data novelty score, calculated as explained in the paper based on the sheet 'Rules Data novelty score'.
Cluster: This column indicates the cluster number as explained in the Discussion section of the paper.
Rules Data novelty score
This sheet contains 15 rows, of which the first contains the titles, and two columns. The first columns contains the input data types as in the previous sheet and the second column contains the respective novelty scores.
Model table
This sheet contains a table of the literature at the end of Phase 4 with indications of model types used in the articles, the model novelty score and the cluster that the articles belong to. It has 95 rows, where each row contains the information of one article, except the last 'Total' row. It contains 21 columns :
ID: Same as before.
Column1: Same as before
Authors: Same as before.
Title: Same as before.
Year: Same as before.
MRA, Kriging, SEM, SVC, Time Series, FL, NN, DT, RF, GBT, SVM, ANN, (Other) Ensembles, DL: These are the different model types. The cell is filled with 'X' if the corresponding article is using the model type described in the column name.
Score: This column indicates the model novelty score, calculated as explained in the paper based on the sheet 'Rules Model novelty score'.
Cluster: This column indicates the cluster number as explained in the Discussion section of the paper.
Rules Model novelty score
This sheet contains 15 rows, of which the first contains the titles, and two columns. The first columns contains the model types as in the previous sheet and the second column contains the respective novelty scores